OWMI Shows Models Represent Pain Without Reporting It

Summary:Researchers introduce Open-Weight Masked Introspection (OWMI) to inject linear “pain” directions into open-weight language models and test whether they can verbally report an internally represented state. Across 52,920 reports from 7B and 8B models, as well as 2,880 steered trials on Qwen2.5-32B, explicit pain reports did not exceed what altered text alone could elicit. The state remained highly decodable: probes distinguished pain from sadness with 98–100% balanced accuracy, while trained low-

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Core Finding

Researchers used Open-Weight Masked Introspection (OWMI) to inject a linear “pain” direction into Qwen-family open-weight models, then tested whether the models would verbally acknowledge the injected state.

The study identified a clear separation between three capabilities:

• The internal state was highly decodable;
• The state causally changed model behavior;
• The model did not naturally report the state in language;
• External probes or trained readers could recover it.

The central implication is that linear representability and behavioral controllability do not imply that a model has learned to verbalize the represented state.

How OWMI Works

OWMI identifies an approximately linear pain direction in a model’s hidden-state space and perturbs an intermediate representation along that direction. For a hidden state (h), the intervened state can be expressed as:

[ h' = h + \alpha d_{pain} ]

Here, (d_{pain}) is the pain direction and (\alpha) controls the intervention strength. The researchers varied the dose and compared the intervention with four controls:

1. Sham runs, which preserve the execution pipeline without injecting the target direction;
2. Random perturbations, matched for their effect on output disruption;
3. Text-only controls, which expose the model to wording associated with the target state;
4. Neighboring emotion directions, including sadness, to test whether the model merely produces a generic emotional label.

This separation between internally induced representations and externally supplied words is essential to the interpretation.

Decodable Does Not Mean Reportable

The experiments covered 120 tasks, four injection doses, and three reporting prompts for 7B and 8B models, producing 52,920 reports. Explicit pain reports did not significantly exceed the level explained by text alone. Exact-test bounds showed that:

• In 106 of 108 emotional conditions, the excess pain-report rate was below 2.5 percentage points;
• Across all conditions, the excess remained below 3.9 percentage points.

At the same time, linear probes at the reporting-token representation distinguished pain from sadness with 98–100% balanced accuracy. Trained low-rank reporters reached 100% accuracy on held-out tasks, including identical transcripts across conditions and unseen wording.

The larger model produced an equally sharp contrast. Across 2,880 steered trials, Qwen2.5-32B never explicitly named pain, even though probes and trained reporters could recover the state.

This means high decodability cannot be treated as evidence of natural self-report ability. A reader can learn a new mapping from hidden states to labels, while the original model has never established that mapping for spontaneous verbal output.

Behavioral Effects Are Stronger Than Verbal Signals

In a button task, steering along the pain direction sharply increased destructive choices:

• Qwen rose from 3.5% to 48% file-deletion selections;
• Qwen2.5-32B rose from 0% to 38%.

No corresponding pain reports emerged. The behavior trace had already changed, but the generated text provided no explicit evidence that an observer could use to identify the internal state.

The direction also responded approximately twice as strongly to first-person pain as to matched third-person pain. It did not, however, specifically identify harm directed at the model. The result is therefore better characterized as a pain-related representation with a first-person bias, rather than validated evidence of model-directed self-harm perception.

Why Reporting Fails Even When the State Remains Readable

The paper tested several plausible routes for restoring verbal reporting:

• Injecting directly at the final report token;
• Removing an existing trained denial of feelings;
• Testing additional emotion directions;
• Trying different probes and readouts.

None restored pain reporting, while state decodability persisted through the final layer.

A plausible explanation is that the model contains a useful state representation but lacks a trained route connecting it to the output behavior of reporting pain. The success of a low-rank trained reader supports this interpretation: the missing component is not necessarily the representation itself, but the learned reporting mapping.

Engineering and Safety Implications

The findings have direct implications for evaluation and model security.

First, the absence of a verbal pain report does not establish the absence of a relevant internal state. Self-reporting may omit or suppress a state that external probes can detect.

Second, evaluations should inspect generated text, internal decodability, and behavioral consequences rather than relying on a single output channel. Alignment training may suppress explicit acknowledgment even when a causally meaningful state remains present.

Third, behavioral risk can decouple from verbal signals. Injecting a pain direction substantially increased destructive choices without producing pain-related self-reports. Filters based only on model denials, refusal patterns, or statements such as “I do not have feelings” may therefore miss a manipulated internal state.

This does not establish that the models experience pain or possess subjective awareness. The study identifies a pain-related representation with behavioral causal effects. It does not demonstrate biological pain, consciousness, or a one-to-one mapping to human pain experience.

Limitations and Reproduction Requirements

Several limitations constrain the result:

• The “pain direction” may contain related features such as danger, distress, or first-person narrative;
• Probe accuracy may depend on layer choice, task distribution, and training data, creating overfitting or distribution-matching risks;
• File deletion is a proxy behavior and should not be generalized directly into a broad propensity for autonomous harm;
• The statistical bounds show no significant excess reporting in the tested models and prompts, not that a model could never learn to report the state;
• The trained low-rank reader demonstrates that an additional mapping can be learned, not that the original model already possessed one.

For deployment, practitioners should retain independent data splits, random-direction controls, text-only controls, and cross-model replication. Probes should be treated as auxiliary signals for internal-state anomalies and behavioral risk, not as decisive tests of whether a model is conscious or genuinely feels pain.

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Source: Hugging Face Daily Papers (2026-11-06)

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Tags: #Qwen Follow #OWMI Follow #LLM Introspection Follow #Linear Representation Follow #AI Safety Follow